Amazon Applied Scientist FAR Frontier AI & Robotics 

United States, California, San Francisco 

539093247

Key job responsibilities
- Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish
- Drive independent research initiatives in robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization
- Lead technical projects from conceptualization through deployment, ensuring robust performance in production environmentsA day in the life
- Design and implement novel foundation model architectures, leveraging our extensive compute infrastructure to train and evaluate at scale- Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems- Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas
- Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications1. Medical, Dental, and Vision Coverage
2. Maternity and Parental Leave Options
3. Paid Time Off (PTO)
4. 401(k) Plan

- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning

- Experience using Unix/Linux
- Experience with popular deep learning frameworks such as MxNet and Tensor FlowPursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.